Comprehensive Global Assessment of 23 Gridded Precipitation Datasets Across 16,295 Catchments Using Hydrological Modeling

Abstract. Numerous gridded precipitation (P) datasets have been developed to address a variety of needs and challenges. However, selecting the most suitable and reliable dataset remains a challenge for users. We conducted the most comprehensive global evaluation to date of gridded (sub-)daily P datasets using hydrological modeling. A total of 23 datasets, derived from satellite, model, gauge sources, or their combinations thereof, were assessed. To evaluate their performance, we calibrated the conceptual hydrological model HBV against observed daily streamflow for 16,295 catchments (each < 10, 000 km²) world- wide, using each P dataset as input. The Kling-Gupta Efficiency (KGE) was used as the performance metric and the calibration score served as a proxy for P dataset performance. Overall, MSWEP V2.8 demonstrated the highest performance (median KGE of 0.75), highlighting the value of merging P estimates from diverse data sources and applying daily gauge corrections. Among the purely satellite-based P datasets, the soil moisture- and microwave-based GPM+SM2RAIN dataset performed best (median KGE of 0.60), while the JRA-3Q reanalysis ranked highest among the purely model-based datasets (median KGE of 0.67), outperforming the widely used ERA5 reanalysis (median KGE of 0.59). Performance varied across Köppen-Geiger climate zones, with the best results in polar (E) regions (median KGE of 0.74 across datasets) and the lowest in arid (B) regions (median KGE of 0.33 across datasets). We further examined the spatial relationships between catchment attributes and KGE scores, identifying potential evaporation, air temperature, solid P fraction, and latitude as the strongest predictors of performance. Our analysis revealed significant regional differences in dataset performance and heterogeneity in P error characteristics, underscoring the critical importance of careful dataset selection for water resource management, hazard assessment, agricultural planning, and environmental monitoring.